Predictive interpretable analytics models for forecasting healthcare costs using open healthcare data by A. Ravishankar Rao, Raunak Jain, Mrityunjai Singh, Rahul Garg

“…These models are explainable. We analyzed features to determine those that were predictive of total costs. …”
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Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery by Caroline Truntzer, Dina Ouahbi, Titouan Huppé, David Rageot, Alis Ilie, Chloe Molimard, Françoise Beltjens, Anthony Bergeron, Angelique Vienot, Christophe Borg, Franck Monnien, Frédéric Bibeau, Valentin Derangère, François Ghiringhelli

“…We estimated a multi-instance learning survival model to predict relapse in the training set and evaluated its performance in the validation set. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…The comprehensive results indicate that the optimized CNN model, featuring four convolutional layers, one hidden layer with 64 neurons, and a dropout rate of 0.5, outperformed the transfer learning models.ConclusionThe findings of this study demonstrate that our developed proposed CNN model provides a high-performance solution for the rapid identification of quince leaf diseases. …”
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Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation by Mi‐Na Kim, Yong Seok Lee, Youngmin Park, Ayoung Jung, Hanjee So, Joonwoong Park, Jin‐Joo Park, Dong‐Joo Choi, So‐Ree Kim, Seong‐Mi Park

“…To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real‐world data. …”
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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

“…The developed app integrates multiple models, compares their predictions for different clinical scenarios, and enhances prediction transparency and reliability.The multi-model approach demonstrates strong predictive performance for DF risk, offering clinicians an intuitive and accurate assessment tool tailored to individual patients. …”
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Wheat Powdery Mildew Severity Classification Based on an Improved ResNet34 Model by Meilin Li, Yufeng Guo, Wei Guo, Hongbo Qiao, Lei Shi, Yang Liu, Guang Zheng, Hui Zhang, Qiang Wang

“…The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. …”
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Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. …”
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LLM4Mat-bench: benchmarking large language models for materials property prediction by Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers, Adji Bousso Dieng

“…Large language models (LLMs) are increasingly being used in materials science. …”
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Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing by Muhammad Zeeshan Khan, Saad Harous, Saleet Ul Hassan, Muhammad Usman Ghani Khan, Razi Iqbal, Shahid Mumtaz

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Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models by Shazly A. Mohamed, Abdullah M. Shaheen, Mohammed H. Alqahtani, Badr M. Al Faiya

“…Abstract Accurate parameter estimation in photovoltaic (PV) models is essential for optimizing solar energy systems, enhancing their efficiency, and ensuring precise performance predictions. …”
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Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China by Yuanyuan Ma, Yi Yang, Xiaoping Mai, Chongjian Qiu, Xiao Long, Chenghai Wang

“…To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. …”
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From Convolution to Attention: Transformer-Based Modeling for Multi-Day Wildfire Spread Forecasting by Parul Dubey, Nitin Rakesh, Pushkar Dubey, Pitshou Ntambu Bokoro

“…We evaluated the model performance using F1-score, IoU, MAE, directional accuracy, and AIC-based feature importance. …”
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Cloud Removal in the Tibetan Plateau Region Based on Self-Attention and Local-Attention Models by Guoqiang Zheng, Tianle Zhao, Yaohui Liu

“…This paper proposes a novel Multi-Scale Attention-based Cloud Removal Model (MATT). The model integrates global and local information by incorporating multi-scale attention mechanisms and local interaction modules, enhancing the contextual semantic relationships and improving the robustness of feature representation. …”
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An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach by Duc-Thinh Pham, Sameer Alam, Vu Duong

“…The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. …”
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Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan‐Cancer by Feng Gao, Junxiang Ding, Baowen Gai, Du Cai, Chuling Hu, Feng‐Ao Wang, Ruikun He, Junwei Liu, Yixue Li, Xiao‐Jian Wu

“…This model assists clinical practitioners in achieving more precise prognosis predictions, particularly when patients lack corresponding molecular features. …”
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Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models by Olushina Olawale Awe, Peter Njoroge Mwangi, Samuel Kotva Goudoungou, Ruth Victoria Esho, Olanrewaju Samuel Oyejide

“…Results Among the ensemble models, Random Forest demonstrated the highest performance with an ROC AUC score of 0.869, followed closely by CatBoost at 0.787. …”
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Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration by Houda Lamane, Latifa Mouhir, Rachid Moussadek, Bouamar Baghdad, Ozgur Kisi, Ali El Bilali

“…Box plot diagrams confirm the enhanced performance of these combined models, and the best-performing ones for the four hydrological stations being the combined RF + GP model at the Aguibat Ziar station, the combined XGBoost + GP model at the Ain Loudah station, the CatBoost model at the Ras Fathia station, and the RF model at the Sidi Med Cherif station. …”
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Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose by Qiong Zou, Borui Chen, Yang Zhang, Xi Wu, Yi Wan, Changsheng Chen

“…The first 10 important features were modelled via random forest (RF) screening. …”
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Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Model performance was evaluated by the area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), and calibration metrics. …”
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Stacked random forest model for colorectal cancer detection using complete blood counts by Junfeng Luo, Weiwei Tan, Shaobo Chen, Yijing Chen, Ya Fu, Xiaojuan Jing, Lingling Kang, Qingyun Li, Zhenjian Ma, Tingji Sun, Peng Xiao, Shigui Xue, Xiaozhi Wang, Houde Zhang

“…Model performance was evaluated using the area under the curve (AUC), specificity, and sensitivity. …”
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